Snowflake SOL-C01 Practice Questions

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Snowflake SOL-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	Snowflake SnowPro Associate - Platform Certification
Exam Number:	SOL-C01
Passing Score:	750/1000
Available Languages:	Japanese, English
Related Certifications:	SnowPro Advanced Data Engineer SnowPro Advanced Architect SnowPro Core Certification
Exam Format:	Multiple Choice, Multiple Select
Exam Duration:	115 minutes
Exam Price:	$175 USD
Certificate Validity Period:	2 years
Real Exam Qty:	80-100
Recommended Training:	Snowflake Learning & Training Snowflake Documentation
Exam Registration:	Snowflake Certifications Portal
Sample Questions:	Snowflake SOL-C01 Sample Questions
Exam Way:	Online proctored exam via Snowflake certification platform or authorized testing provider.
Pre Condition:	No formal prerequisites required; basic knowledge of data warehousing and SQL recommended.
Official Syllabus URL:	https://www.snowflake.com/en/certifications/

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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
Topic 2	Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
Topic 3	Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
Topic 4	Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.

Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are loading data into a Snowflake table 'EMPLOYEE DATA (EMP_ID INT, EMP NAME VARCHAR, DEPARTMENT VARCHAR)' using 'INSERT' statements. You notice that some of the
'EMP NAME values in your source data contain single quotes ('). Which of the following methods can you use to ensure that these values are correctly inserted into the table without causing syntax errors?

A. Use parameterized queries or prepared statements to insert the data.
B. Remove special characters and single quotes from all EMP_NAME.
C. Enclose the ` EMP_NAME values in double quotes (") instead of single quotes in the `INSERT statement.
D. Replace each single quote (') with two single quotes (") in the `EMP NAME' values before inserting.
E. Before inserting, use 'REPLACE(EMP NAME, "", in the INSERT statement to escape the single quotes.

Answer: A,D

Explanation:
Option A is a standard SQL practice to escape single quotes within a string literal by replacing them with two single quotes. Option C is another secure and effective approach. Parameterized queries or prepared statements handle the escaping of special characters automatically, preventing SQL injection vulnerabilities and ensuring data integrity. Option B doesn't work. In Snowflake, you can't use double quotes to escape single quotes within a string literal. Option D will work in some SQL dialects but may not work in all versions. Option E changes the meaning of your data.

NEW QUESTION # 37
Consider the following SQL code snippet intended to insert data into a Snowflake table named
'employees'. However, the execution results in a data type mismatch error. Analyze the code and identify the root cause of the error. Assume the 'salary' column in the 'employees' table is defined as NUMBER(10,2).

A. The date format 'YYYY-MM-DD' is incorrect for the 'hire date' column.
B. The 'insert' statement is missing the column names explicitly.
C. The string 'Five Thousand' cannot be implicitly converted to a NUMBER(10,2) for the 'salary' column.
D. The 'dept_id' is not a valid integer value.
E. The semi-colon (;) at the end of the VALUES clause is causing a syntax error.

Answer: C

Explanation:
The error is due to attempting to insert the string 'Five Thousand' into the 'salary' column, which is defined as NUMBER(10,2). Snowflake cannot implicitly convert a string representation of a number written in words to a numerical value. All other options are syntactically correct or would result in different types of errors.

NEW QUESTION # 38
What does "warehouse scaling up/down" refer to in Snowflake?

A. Adjusting the number of clusters in a multi-cluster warehouse.
B. Changing the size of the warehouse (e.g., from Small to Medium or Vice Versa).
C. Moving data between different storage locations
D. Changing the region of the warehouse

Answer: B

Explanation:
Scalingup or downrefers tovertical scaling, meaning the warehouse's compute size is increased or decreased.
For example, moving fromSmall # Medium # Largeincreases CPU, memory, and I/O capacity, enabling faster processing for compute-intensive workloads.
Vertical scaling improves single-query performance, large ETL jobs, complex joins, or transformations. It does not improve concurrency unless multi-cluster mode is also used.
Horizontal scaling (scaling out/in), by contrast, adjusts thenumber of clustersand is used for concurrency.
Region selection is fixed at account creation and cannot be changed by resizing a warehouse. Storage movement is unrelated to compute rescaling.

NEW QUESTION # 39
A data scientist is working on a machine learning project using Snowflake Notebooks. They have a dataset stored in Snowflake and need to perform feature engineering. They want to write a Python function that takes a Snowflake table name and a list of SQL expressions as input, executes these expressions against the table, and returns a Pandas DataFrame containing the new features. Which approach is MOST suitable for creating and executing this function within a Snowflake Notebook, minimizing data transfer outside of Snowflake?

A. Use the `snowflake.connectors library to connect to Snowflake, execute each SQL expression using the method, and append the results to a Pandas DataFrame.
B. Use the `snowflake.snowpark.functions.call_udf to call a UDF from Snowflake notebooks and create a dataframe.
C. Utilize the `sqlalchemy' library to establish a connection to Snowflake, construct the SQL query dynamically using SQLAlchemy's expression language, and fetch the results into a Pandas DataFrame.
D. Use the Snowflake web UI to create a view containing the feature engineered data. Load that view into the notebook as a Pandas DataFrame.
E. Create a Snowflake User-Defined Function (UDF) that encapsulates the feature engineering logic, and then call this UDF from the notebook using Snowpark or standard SQL.

Answer: E

Explanation:
Creating a UDF is the most efficient way to perform feature engineering because the computation happens within Snowflake's compute engine. Snowpark's or standard SQL can then be used to call and retrieve data from UDFs. This minimizes data transfer. Option A still fetches intermediate data. Options D create extra steps and extra objects. Option E isn't the standard approach when working with Snowpark in Notebooks.

NEW QUESTION # 40
A Snowflake architect is designing a data lake solution using Snowflake's external tables. The data lake contains JSON files stored in AWS S3. The architect needs to ensure that Snowflake can automatically discover new JSON files added to the S3 bucket and make them available for querying through the external table. Which of the following approaches provides the MOST efficient and scalable solution for automatically refreshing the external table metadata?
Schedule a daily task in Snowflake to execute 'ALTER EXTERNAL TABLE REFRESH' to manually update the metadata.

A. Enable Snowflake's Event Tables and configure them to capture S3 events. Then, create a stream on the Event Table and a task that triggers 'ALTER EXTERNAL TABLE REFRESH' whenever a new event is detected in the stream. Requires Snowflake Enterprise Edition or higher.
B. Utilize Snowflake's automatic data clustering feature on the external table based on the file creation timestamp in S3. Snowflake will automatically update the metadata when new files are detected.
C. Configure an AWS Lambda function triggered by S3 events (e.g., `s3:ObjectCreated') to execute
'ALTER EXTERNAL TABLE REFRESH' via the Snowflake API whenever a new file is added to the S3 bucket.
D. Use Snowflake's Snowpipe with auto-ingest configured to automatically load data from the S3 bucket into a Snowflake internal stage and then use the COPY INTO statement to load the data into a regular Snowflake table.

Answer: D

Explanation:
Using Event Tables (Option C) is the most efficient and scalable solution. Snowflake Event Tables are designed to capture and process events directly within Snowflake, making them ideal for this scenario. The stream and task can automatically trigger the refresh whenever a new S3 event is detected. Option A (scheduled task) is less efficient. Option B (Lambda function) adds external complexity. Option D (Snowpipe) loads data into Snowflake, not updating external table metadata. Option E (automatic data clustering) does not automatically update the metadata of external tables when new files are added.

NEW QUESTION # 41
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